K-means-based fuzzy classifier design
學年 88
學期 2
發表日期 2000-05-07
作品名稱 K-means-based fuzzy classifier design
作品名稱(其他語言)
著者 翁慶昌; Wong, Ching-chang; Chen, Chia-chong; Yeh, Shih-liang
作品所屬單位 淡江大學電機工程學系
出版者 Institute of Electrical and Electronics Engineers (IEEE)
會議名稱 Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on
會議地點 San Antonio, TX, USA
摘要 In this paper, a method based on the K-means algorithm is proposed to efficiently design a fuzzy classifier so that the training patterns can be correctly classified by the proposed approach. In this method, the K-means algorithm is first used to partition the training data for each class into several clusters, and the cluster center and the radius for each cluster are calculated. Then, a fuzzy system design method that uses a fuzzy rule to represent a cluster is proposed such that a fuzzy classifier can be efficiently constructed to correctly classify the training data. The proposed method has the following features: 1) it does not need prior parameter definition; 2) it only needs a short training time; and 3) it is simple. Finally, two examples are used to illustrate and examine the proposed method for the fuzzy classifier design
關鍵字
語言 en
收錄於
會議性質 國際
校內研討會地點
研討會時間 20000507~20000507
通訊作者
國別 USA
公開徵稿 Y
出版型式 紙本
出處 Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on (Volume:1 ), pp.48-52
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